Pyyan / Compare / Neo4j vs LlamaIndex vs LangChain & LangGraph
Neo4j vs LlamaIndex vs LangChain & LangGraph
3 of 5RAG & Knowledge Graphs · verified 13 Aug 2026
| Specification | Neo4j | LlamaIndex | LangChain & LangGraph |
|---|---|---|---|
| Summary | The graph database most knowledge graphs are stored in. | The strongest option for document-centric retrieval. | Orchestration, with retrieval as one piece. |
| Kind | Database | Framework | Framework |
| Graph | Yes, native | Yes, property graph index | Via integrations |
| Language | Cypher | Python, TypeScript | Python, TypeScript |
| Licence | GPL-3.0 / commercial | MIT | MIT |
| GitHub stars | ~14k | ~45k | ~120k |
| Category | RAG & Knowledge Graphs | RAG & Knowledge Graphs | RAG & Knowledge Graphs |
| Official | Neo4j ↗ | LlamaIndex ↗ | LangChain ↗ |
Highlighted rows are where these differ.
Neo4j
- Native vector index alongside graph traversal
- GraphRAG and Graphiti both commonly sit on it
Best for the storage layer under a knowledge graph.
LlamaIndex
- Deep document handling and many indexing strategies
- The common advice is LlamaIndex for retrieval, LangGraph for orchestration
Best for PDFs, knowledge bases, structured data.
LangChain & LangGraph
- LangGraph adds durable state and checkpointing
- Widely used, and widely argued about
Best for agents that retrieve as one step among many.